Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
Degel MA, Navab N, Albarqouni S (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11073 LNCS
Pages Range: 630-637
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030009366
DOI: 10.1007/978-3-030-00937-3_72
Abstract
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is desirable, since manual delineations are time-consuming, challenging and observer-dependent. Convolutional neural networks have made improvements in computer vision and in medical image analysis. They have successfully been applied to segmentation tasks and were extended to work on volumetric data. In this paper we introduce a combined deep-learning based approach on volumetric segmentation in Ultrasound acquisitions with incorporation of prior knowledge about left atrial shape and imaging device. The results show, that including a shape prior helps the domain adaptation and the accuracy of segmentation is further increased with adversarial learning.
Involved external institutions
Germany (DE)How to cite
APA:
Degel, M.A., Navab, N., & Albarqouni, S. (2018). Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound. In Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 630-637). Granada, ESP: Springer Verlag.
MLA:
Degel, Markus A., Nassir Navab, and Shadi Albarqouni. "Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos, Springer Verlag, 2018. 630-637.
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